OpenRaiser/Intern-Atlas huggingface dataset:
Infrastructure Node-based Vehicle Localization for Autonomous Driving
Venue: arXiv cs.RO 2021
Year: 2021

Vehicle localization is essential for autonomous vehicle (AV) navigation and Advanced Driver Assistance Systems (ADAS). Accurate vehicle localization is often achieved via expensive inertial navigation systems or by employing compute-intensive vision processing (LiDAR/camera) to augment the low-cost and noisy inertial sensors. Here we have developed a framework for fusing the information obtained from a smart infrastructure node (ix-node) with the autonomous vehicles on-board localization engine to estimate the robust and accurate pose of the ego-vehicle even with cheap inertial sensors. A smart ix-node is typically used to augment the perception capability of an autonomous vehicle, especially when the onboard perception sensors of AVs are blocked by the dynamic and static objects in the environment thereby making them ineffectual. In this work, we utilize this perception output from an ix-node to increase the localization accuracy of the AV. The fusion of ix-node perception output with the vehicle's low-cost inertial sensors allows us to perform reliable vehicle localization without the need for relying on expensive inertial navigation systems or compute-intensive vision processing onboard the AVs. The proposed approach has been tested on real-world datasets collected from a test track in Ann Arbor, Michigan. Detailed analysis of the experimental results shows that incorporating ix-node data improves localization performance.
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Venue: arXiv cs.CL 2021
Year: 2021

Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in Euclidean space, while taxonomies, inherently hierarchical, more naturally align with the geometric properties of a hyperbolic space. In this paper, we present HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN). Specifically, HyperExpan leverages position embeddings to exploit the structure of the existing taxonomies, and characterizes the concept profile information to support the inference on unseen concepts during training. Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
Venue: arXiv cs.AI 2021
Year: 2021

In recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while being able to deploy and test reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines are deployed consists of various algorithms. The test benchmark and baselines are to provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing the progress of RL-based methods for intersection autonomous driving control. The code of our proposed framework can be found at https://github.com/liuyuqi123/ComplexUrbanScenarios.
Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
Venue: arXiv cs.IR 2021
Year: 2021

Web search heavily relies on click-through behavior as an essential feedback signal for performance improvement and evaluation. Traditionally, click is usually treated as a positive implicit feedback signal of relevance or usefulness, while non-click (especially non-click after examination) is regarded as a signal of irrelevance or uselessness. However, there are many cases where users do not click on any search results but still satisfy their information need with the contents of the results shown on the Search Engine Result Page (SERP). This raises the problem of measuring result usefulness and modeling user satisfaction in "Zero-click" search scenarios. Previous works have solved this issue by (1) detecting user satisfaction for abandoned SERP with context information and (2) considering result-level click necessity with external assessors' annotations. However, few works have investigated the reason behind non-click behavior and estimated the usefulness of non-click results. A challenge for this research question is how to collect valuable feedback for non-click results. With neuroimaging technologies, we design a lab-based user study and reveal differences in brain signals while examining non-click search results with different usefulness levels. The findings in significant brain regions and electroencephalogram~(EEG) spectrum also suggest that the process of usefulness judgment might involve similar cognitive functions of relevance perception and satisfaction decoding. Inspired by these findings, we conduct supervised learning tasks to estimate the usefulness of non-click results with brain signals and conventional information (i.e., content and context factors). Results show that it is feasible to utilize brain signals to improve usefulness estimation performance and enhancing human-computer interactions in "Zero-click" search scenarios.
Audio-Visual Grounding Referring Expression for Robotic Manipulation
Venue: arXiv cs.RO 2021
Year: 2021

Referring expressions are commonly used when referring to a specific target in people's daily dialogue. In this paper, we develop a novel task of audio-visual grounding referring expression for robotic manipulation. The robot leverages both the audio and visual information to understand the referring expression in the given manipulation instruction and the corresponding manipulations are implemented. To solve the proposed task, an audio-visual framework is proposed for visual localization and sound recognition. We have also established a dataset which contains visual data, auditory data and manipulation instructions for evaluation. Finally, extensive experiments are conducted both offline and online to verify the effectiveness of the proposed audio-visual framework. And it is demonstrated that the robot performs better with the audio-visual data than with only the visual data.
Realism of Simulation Models in Serious Gaming: Two case studies from Urban Water Management Higher Education
Venue: arXiv cs.MM 2021
Year: 2021

For games used in educational contexts, realism, i.e., the degree of congruence between the simulation models used in the games and the real-world systems represented, is an important characteristic for achieving learning goals well. However, in the past, the realism of especially entertainment games has often been identified as insufficient. Thus, this study is investigating the degree of realism provided by current games. To this purpose, two games in the domain urban water management, a subdomain of environmental engineering (EE), are examined. One is ANAWAK, a web-based serious game on water management and climate change. For ANAWAK, an analysis of the simulation model is conducted. Second, the simulation model of the entertainment game Cities: Skylines (CS) is analyzed. In addition, a survey among CS players (N=61) is conducted. Thereby, different degrees of realism in various EE subdomains are revealed. All in all, there are still considerable deficits regarding the degree of realism in the CS simulation model. However, modding as a means of achieving more realistic simulation models is more widely supported than in the past.
Emulating Aerosol Microphysics with Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average $R^2$ score of $89\%$. On a GPU we achieve a speed-up of 120 compared to the original model.
Adaptive Neural Message Passing for Inductive Learning on Hypergraphs
Venue: arXiv cs.LG 2021
Year: 2021

Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawback is mitigated by hypergraphs, in which an edge can connect an arbitrary number of nodes. Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperMSG, a novel hypergraph learning framework that uses a modular two-level neural message passing strategy to accurately and efficiently propagate information within each hyperedge and across the hyperedges. HyperMSG adapts to the data and task by learning an attention weight associated with each node's degree centrality. Such a mechanism quantifies both local and global importance of a node, capturing the structural properties of a hypergraph. HyperMSG is inductive, allowing inference on previously unseen nodes. Further, it is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets. Finally, we demonstrate the effectiveness of HyperMSG in learning multimodal relations through detailed experimentation on a challenging multimedia dataset.
Differentiable Surface Triangulation
Venue: arXiv cs.CV 2021
Year: 2021

Triangle meshes remain the most popular data representation for surface geometry. This ubiquitous representation is essentially a hybrid one that decouples continuous vertex locations from the discrete topological triangulation. Unfortunately, the combinatorial nature of the triangulation prevents taking derivatives over the space of possible meshings of any given surface. As a result, to date, mesh processing and optimization techniques have been unable to truly take advantage of modular gradient descent components of modern optimization frameworks. In this work, we present a differentiable surface triangulation that enables optimization for any per-vertex or per-face differentiable objective function over the space of underlying surface triangulations. Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation. We translate this result into a computational algorithm by proposing a soft relaxation of the classical weighted Delaunay triangulation and optimizing over vertex weights and vertex locations. We extend the algorithm to 3D by decomposing shapes into developable sets and differentiably meshing each set with suitable boundary constraints. We demonstrate the efficacy of our method on various planar and surface meshes on a range of difficult-to-optimize objective functions. Our code can be found online: https://github.com/mrakotosaon/diff-surface-triangulation.
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a network with MC dropout and a single input, multiple outputs can be sampled. Various methods can be used to obtain epistemic uncertainty maps from those multiple outputs. In the case of multi-class segmentation, the number of methods is even larger as epistemic uncertainty can be computed voxelwise per class or voxelwise per image. This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel). We applied this quantitative analysis to a multi-class segmentation of the carotid artery lumen and vessel wall, on a multi-center, multi-scanner, multi-sequence dataset of (MR) images. We validated our analysis over 144 sets of hyperparameters of a model. Our main analysis considers the relationship between the order of the voxels sorted according to their epistemic uncertainty values and the misclassification of the prediction. Under this consideration, the comparison of combined uncertainty maps reveals that the multi-class entropy and the multi-class mutual information statistically out-perform the other combined uncertainty maps under study. In a class-specific scenario, the one-versus-all entropy statistically out-performs the class-wise entropy, the class-wise variance and the one versus all mutual information. The class-wise entropy statistically out-performs the other class-specific uncertainty maps in terms of calibration. We made a python package available to reproduce our analysis on different data and tasks.
A Deep Learning Perspective on Connected Automated Vehicle (CAV) Cybersecurity and Threat Intelligence
Venue: arXiv cs.CR 2021
Year: 2021

The automation and connectivity of CAV inherit most of the cyber-physical vulnerabilities of incumbent technologies such as evolving network architectures, wireless communications, and AI-based automation. This book chapter entails the cyber-physical vulnerabilities and risks that originated in IT, OT, and the physical domains of the CAV ecosystem, eclectic threat landscapes, and threat intelligence. To deal with the security threats in high-speed, high dimensional, multimodal data and assets from eccentric stakeholders of the CAV ecosystem, this chapter presents and analyzes some of the state of art deep learning-based threat intelligence for attack detection. The frontiers in deep learning, namely Meta-Learning and Federated Learning, along with their challenges have been included in the chapter. We have proposed, trained, and tested the deep CNN-LSTM architecture for CAV threat intelligence; assessed and compared the performance of the proposed model against other deep learning algorithms such as DNN, CNN, LSTM. Our results indicate the superiority of the proposed model although DNN and 1d-CNN also achieved more than 99% of accuracy, precision, recall, f1-score, and AUC on the CAV-KDD dataset. The good performance of deep CNN-LSTM comes with the increased model complexity and cumbersome hyperparameters tuning. Still, there are open challenges on deep learning adoption in the CAV cybersecurity paradigm due to lack of properly developed protocols and policies, poorly defined privileges between stakeholders, costlier training, adversarial threats to the model, and poor generalizability of the model under out of data distributions.
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
Venue: arXiv cs.CV 2021
Year: 2021

We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multivariate Gaussian posterior through the probabilistic convolutional encoder and the likelihood distribution with the probabilistic convolutional decoder; and optimizes cluster labels assignment. Here, the learned multivariate Gaussian posterior captures the latent distribution of a large set of unlabeled images. Then, we perform unsupervised clustering on top of the variational latent space using a clustering loss. In this approach, the probabilistic decoder helps to prevent the distortion of data points in the latent space and to preserve the local structure of data generating distribution. The training process can be considered as a self-training process to refine the latent space and simultaneously optimizing cluster assignments iteratively. We evaluated our proposed framework on three public datasets that represented different medical imaging modalities. Our experimental results show that our proposed framework generalizes better across different datasets. It achieves compelling results on several medical imaging benchmarks. Thus, our approach offers potential advantages over conventional deep unsupervised learning in real-world applications. The source code of the method and all the experiments are available publicly at: https://github.com/csfarzin/DVC
Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi
Venue: arXiv cs.NE 2021
Year: 2021

Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational acceleration for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, rigorous simulation and consequent validation of brain-based experimental data is imperative. In this work, we investigate the potential of Intel's fifth generation neuromorphic chip - `Loihi', which is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. The work is implemented in context of simulating the Leaky Integrate and Fire (LIF) models based on the mouse primary visual cortex matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on the classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
Security Analysis of Capsule Network Inference using Horizontal Collaboration
Venue: arXiv cs.LG 2021
Year: 2021

The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was proposed to address these challenges because its architecture can encode and preserve the spatial orientation of input images. Similar to traditional CNNs, CapsNet is also vulnerable to several malicious attacks, as studied by several researchers in the literature. However, most of these studies focus on single-device-based inference, but horizontally collaborative inference in state-of-the-art systems, like intelligent edge services in self-driving cars, voice controllable systems, and drones, nullify most of these analyses. Horizontal collaboration implies partitioning the trained CNN models or CNN tasks to multiple end devices or edge nodes. Therefore, it is imperative to examine the robustness of the CapsNet against malicious attacks when deployed in horizontally collaborative environments. Towards this, we examine the robustness of the CapsNet when subjected to noise-based inference attacks in a horizontal collaborative environment. In this analysis, we perturbed the feature maps of the different layers of four DNN models, i.e., CapsNet, Mini-VGG, LeNet, and an in-house designed CNN (ConvNet) with the same number of parameters as CapsNet, using two types of noised-based attacks, i.e., Gaussian Noise Attack and FGSM noise attack. The experimental results show that similar to the traditional CNNs, depending upon the access of the attacker to the DNN layer, the classification accuracy of the CapsNet drops significantly. For example, when Gaussian Noise Attack classification is performed at the DigitCap layer of the CapsNet, the maximum classification accuracy drop is approximately 97%.
Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization
Venue: arXiv cs.NE 2021
Year: 2021

Spiking neural networks are a promising approach towards next-generation models of the brain in computational neuroscience. Moreover, compared to classic artificial neural networks, they could serve as an energy-efficient deployment of AI by enabling fast computation in specialized neuromorphic hardware. However, training deep spiking neural networks, especially in an unsupervised manner, is challenging and the performance of a spiking model is significantly hindered by dead or bursting neurons. Here, we apply end-to-end learning with membrane potential-based backpropagation to a spiking convolutional auto-encoder with multiple trainable layers of leaky integrate-and-fire neurons. We propose bio-inspired regularization methods to control the spike density in latent representations. In the experiments, we show that applying regularization on membrane potential and spiking output successfully avoids both dead and bursting neurons and significantly decreases the reconstruction error of the spiking auto-encoder. Training regularized networks on the MNIST dataset yields image reconstruction quality comparable to non-spiking baseline models (deterministic and variational auto-encoder) and indicates improvement upon earlier approaches. Importantly, we show that, unlike the variational auto-encoder, the spiking latent representations display structure associated with the image class.
DARSI: An Ontology for Facilitating the Development of Data Sharing and Use Agreements
Venue: arXiv cs.DL 2021
Year: 2021

The advantages of data sharing across organizations and disciplines are indisputable; although, sensitive and restricted data cannot be easily shared due to policies and legal matters. The research presented in this paper takes a step toward systematizing the sharing of sensitive and restricted research data by developing an ontology to frame and guide DSUA (Data Sharing and Usage Agreement) development. The paper provides background context, describes the ontology creation process, and introduces the Data Sharing Agreements for Restricted and Sensitive Information (DARSI) ontology. DARSI contains four top level classes, 20 sub-classes, 33 sub-categories, and 17 simple properties for categories applicable at various levels. The discussion provides further insight into the work accomplished, and the conclusion identifies next steps.